IPDxIRR_2F (Ionospheric plasma densities)

Abstract: Access to the derived plasma characteristics at 1Hz (level 2 product).

%load_ext watermark
%watermark -i -v -p viresclient,pandas,xarray,matplotlib

Python implementation: CPython
Python version       : 3.11.6
IPython version      : 8.18.0

viresclient: 0.15.2
pandas     : 2.1.3
xarray     : 2023.12.0
matplotlib : 3.8.2

from viresclient import SwarmRequest
import datetime as dt
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter

request = SwarmRequest()

IPDxIRR_2F product information

Derived plasma characteristics at 1Hz, for each Swarm spacecraft.

Documentation:

• Swarm Handbook: IPDxIRR_2F

• Product specification

Check what “IPD” data variables are available

request.available_collections("IPD", details=False)

{'IPD': ['SW_OPER_IPDAIRR_2F', 'SW_OPER_IPDBIRR_2F', 'SW_OPER_IPDCIRR_2F']}

request.available_measurements("IPD")

['Ne',
 'Te',
 'Background_Ne',
 'Foreground_Ne',
 'PCP_flag',
 'Grad_Ne_at_100km',
 'Grad_Ne_at_50km',
 'Grad_Ne_at_20km',
 'Grad_Ne_at_PCP_edge',
 'ROD',
 'RODI10s',
 'RODI20s',
 'delta_Ne10s',
 'delta_Ne20s',
 'delta_Ne40s',
 'Num_GPS_satellites',
 'mVTEC',
 'mROT',
 'mROTI10s',
 'mROTI20s',
 'IBI_flag',
 'Ionosphere_region_flag',
 'IPIR_index',
 'Ne_quality_flag',
 'TEC_STD']

Fetch three hours of IPD data

request.set_collection("SW_OPER_IPDAIRR_2F")
request.set_products(measurements=request.available_measurements("IPD"))
data = request.get_between(dt.datetime(2014, 12, 21, 0), dt.datetime(2014, 12, 21, 3))

Load and plot using pandas/matplotlib

df = data.as_dataframe()
df.head()

	ROD	Foreground_Ne	Ne	RODI10s	Te	Latitude	Ionosphere_region_flag	Grad_Ne_at_20km	Longitude	Grad_Ne_at_100km	...	Background_Ne	Grad_Ne_at_50km	PCP_flag	Ne_quality_flag	Radius	Spacecraft	RODI20s	delta_Ne20s	TEC_STD	delta_Ne10s
Timestamp
2014-12-21 00:00:00.196999936	0.0	1305371.000	1255163.2	10238.517220	2212.278353	-4.693533	0	-1.047788	-128.771412	-0.084919	...	1343599.375	-0.403940	0	20000	6.840395e+06	A	7764.002532	10266.500	3.131451	67.875
2014-12-21 00:00:01.196999936	0.0	1292046.000	1250357.7	3263.138721	2165.194729	-4.757416	0	0.338403	-128.772618	-0.144009	...	1343599.375	0.144877	0	20000	6.840404e+06	A	7181.496228	2830.850	3.122494	12961.600
2014-12-21 00:00:02.196999936	0.0	1312436.750	1265851.3	3263.138721	1544.874194	-4.821298	0	0.133643	-128.773822	-0.058276	...	1343599.375	-0.123734	0	20000	6.840413e+06	A	7181.496228	0.000	3.113830	0.000
2014-12-21 00:00:03.196999936	0.0	1312436.750	1312436.8	3263.138721	1228.501871	-4.885179	0	1.443077	-128.775026	-0.144613	...	1343599.375	-0.131441	0	20000	6.840422e+06	A	7390.308480	2194.925	3.104259	12393.550
2014-12-21 00:00:04.196999936	0.0	1315059.375	1253999.0	3263.138721	2681.512355	-4.949060	0	-1.948789	-128.776229	-0.039358	...	1343599.375	-0.403369	0	20000	6.840430e+06	A	7554.331699	9491.525	3.097484	21700.700

5 rows × 29 columns

df.columns

Index(['ROD', 'Foreground_Ne', 'Ne', 'RODI10s', 'Te', 'Latitude',
       'Ionosphere_region_flag', 'Grad_Ne_at_20km', 'Longitude',
       'Grad_Ne_at_100km', 'mROT', 'delta_Ne40s', 'mVTEC', 'IPIR_index',
       'mROTI20s', 'Grad_Ne_at_PCP_edge', 'Num_GPS_satellites', 'mROTI10s',
       'IBI_flag', 'Background_Ne', 'Grad_Ne_at_50km', 'PCP_flag',
       'Ne_quality_flag', 'Radius', 'Spacecraft', 'RODI20s', 'delta_Ne20s',
       'TEC_STD', 'delta_Ne10s'],
      dtype='object')

fig, axes = plt.subplots(nrows=7, ncols=1, figsize=(20, 18), sharex=True)
df.plot(ax=axes[0], y=["Background_Ne", "Foreground_Ne", "Ne"], alpha=0.8)
df.plot(ax=axes[1], y=["Grad_Ne_at_100km", "Grad_Ne_at_50km", "Grad_Ne_at_20km"])
df.plot(ax=axes[2], y=["RODI10s", "RODI20s"])
df.plot(ax=axes[3], y=["ROD"])
df.plot(ax=axes[4], y=["mROT"])
df.plot(ax=axes[5], y=["delta_Ne10s", "delta_Ne20s", "delta_Ne40s"])
df.plot(ax=axes[6], y=["mROTI20s", "mROTI10s"])
axes[0].set_ylabel("[cm$^{-3}$]")
axes[1].set_ylabel("[cm$^{-3}$m$^{-1}$]")
axes[2].set_ylabel("[cm$^{-3}$s$^{-1}$]")
axes[3].set_ylabel("[cm$^{-3}$m$^{-1}$]")
axes[4].set_ylabel("[TECU s$^{-1}$]")
axes[5].set_ylabel("[cm$^{-3}$m$^{-1}$]")
axes[6].set_ylabel("[TECU s$^{-1}$]")
axes[6].set_xlabel("Timestamp")

for ax in axes:
    # Reformat time axis
    # https://www.earthdatascience.org/courses/earth-analytics-python/use-time-series-data-in-python/customize-dates--matplotlib-plots-python/
    ax.xaxis.set_major_formatter(DateFormatter("%Y-%m-%d\n%H:%M:%S"))
    ax.legend(loc="upper right")
    ax.grid()
fig.subplots_adjust(hspace=0)

Load as xarray

ds = data.as_xarray()
ds

<xarray.Dataset>
Dimensions:                 (Timestamp: 10800)
Coordinates:
  * Timestamp               (Timestamp) datetime64[ns] 2014-12-21T00:00:00.19...
Data variables: (12/29)
    Spacecraft              (Timestamp) object 'A' 'A' 'A' 'A' ... 'A' 'A' 'A'
    ROD                     (Timestamp) float64 0.0 0.0 ... 7.28e+03 7.28e+03
    Foreground_Ne           (Timestamp) float64 1.305e+06 ... 6.488e+05
    Ne                      (Timestamp) float64 1.255e+06 1.25e+06 ... 6.468e+05
    RODI10s                 (Timestamp) float64 1.024e+04 3.263e+03 ... 503.7
    Te                      (Timestamp) float64 2.212e+03 ... 1.723e+03
    ...                      ...
    Ne_quality_flag         (Timestamp) int32 20000 20000 20000 ... 10000 10000
    Radius                  (Timestamp) float64 6.84e+06 6.84e+06 ... 6.835e+06
    RODI20s                 (Timestamp) float64 7.764e+03 7.181e+03 ... 907.9
    delta_Ne20s             (Timestamp) float64 1.027e+04 ... 1.702e+03
    TEC_STD                 (Timestamp) float64 3.131 3.122 ... 2.866 2.891
    delta_Ne10s             (Timestamp) float64 67.88 1.296e+04 ... 1.702e+03
Attributes:
    Sources:         ['SW_OPER_IPDAIRR_2F_20141221T000000_20141221T235959_0302']
    MagneticModels:  []
    AppliedFilters:  []

xarray.Dataset

• Dimensions:
  • Timestamp: 10800
• Coordinates: (1)
  • Timestamp
    (Timestamp)
    datetime64[ns]
    2014-12-21T00:00:00.196999936 .....

    description :

    CDF_EPOCH of the measurement.

    array(['2014-12-21T00:00:00.196999936', '2014-12-21T00:00:01.196999936',
           '2014-12-21T00:00:02.196999936', ..., '2014-12-21T02:59:57.196999936',
           '2014-12-21T02:59:58.196999936', '2014-12-21T02:59:59.196999936'],
          dtype='datetime64[ns]')

• Data variables: (29)
  • Spacecraft
    (Timestamp)
    object
    'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'

    units :

    -

    description :

    Spacecraft identifier (values: 'A', 'B', 'C' or '-' if not available).

    array(['A', 'A', 'A', ..., 'A', 'A', 'A'], dtype=object)

  • ROD
    (Timestamp)
    float64
    0.0 0.0 0.0 ... 7.28e+03 7.28e+03

    units :

    cm^-3/s

    description :

    Rate Of change of Density

    array([   0. ,    0. ,    0. , ..., 7201.8, 7279.6, 7279.6])

  • Foreground_Ne
    (Timestamp)
    float64
    1.305e+06 1.292e+06 ... 6.488e+05

    units :

    cm^-3

    description :

    Foreground density, as calculated from ndens using a percentile filter of 7 datapoints (2 Hz raw data) and 50 as the percentile.

    array([1305371.   , 1292046.   , 1312436.75 , ...,  635649.   ,
            643381.5  ,  648826.625])

  • Ne
    (Timestamp)
    float64
    1.255e+06 1.25e+06 ... 6.468e+05

    units :

    cm^-3

    description :

    Plasma density, directly copied from the Langmuir probe files.

    array([1255163.2, 1250357.7, 1265851.3, ...,  631863.3,  639288.8,
            646784.8])

  • RODI10s
    (Timestamp)
    float64
    1.024e+04 3.263e+03 ... 654.8 503.7

    units :

    cm^-3/s

    description :

    Rate Of change of Density Index (RODI) is the standard deviation of ROD over 10 seconds.

    array([10238.51721984,  3263.13872093,  3263.13872093, ...,
             744.15587296,   654.82993341,   503.71589016])

  • Te
    (Timestamp)
    float64
    2.212e+03 2.165e+03 ... 1.723e+03

    units :

    K

    description :

    Electron temperature, directly copied from the Langmuir probe files.

    array([2212.27835299, 2165.19472943, 1544.8741945 , ..., 1730.92749345,
           1727.82763148, 1722.59162917])

  • Latitude
    (Timestamp)
    float64
    -4.694 -4.757 ... 24.74 24.68

    units :

    deg

    description :

    Position in ITRF - Latitude.

    array([-4.69353277, -4.75741624, -4.82129755, ..., 24.80658022,
           24.7426353 , 24.67869043])

  • Ionosphere_region_flag
    (Timestamp)
    int32
    0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0

    units :

    -

    description :

    0: equator, 1: mid-latitudes; 2: auroral oval; 3: polar cap.

    array([0, 0, 0, ..., 0, 0, 0], dtype=int32)

  • Grad_Ne_at_20km
    (Timestamp)
    float64
    -1.048 0.3384 ... 1.002 0.9148

    units :

    cm^-3/m

    description :

    The electron density gradient in a running window calculated via linear regression over 5 data points for the 2 Hz electron density data.

    array([-1.047788  ,  0.33840267,  0.13364267, ...,  0.91515733,
            1.00201333,  0.91482933])

  • Longitude
    (Timestamp)
    float64
    -128.8 -128.8 ... -175.4 -175.4

    units :

    deg

    description :

    Position in ITRF - Longitude.

    array([-128.77141179, -128.77261789, -128.77382227, ..., -175.36866165,
           -175.3692556 , -175.36985325])

  • Grad_Ne_at_100km
    (Timestamp)
    float64
    -0.08492 -0.144 ... 0.9369 0.9621

    units :

    cm^-3/m

    description :

    The electron density gradient in a running window calculated via linear regression over 27 data points for the 2 Hz electron density data.

    array([-0.08491926, -0.14400869, -0.05827591, ...,  0.91132596,
            0.93688094,  0.96208904])

  • mROT
    (Timestamp)
    float64
    -0.01101 -0.01101 ... 0.09621

    units :

    TECU/s

    description :

    Median of Rate Of change of TEC (ROT) from all available GPS satellites above 30 degrees.

    array([-0.01101323, -0.01101323, -0.00883297, ...,  0.09318886,
            0.09159881,  0.0962132 ])

  • delta_Ne40s
    (Timestamp)
    float64
    3.811e+03 1.634e+04 ... 1.702e+03

    units :

    cm^-3

    description :

    Derived by subtracting Ne by its median filtered value in 40 seconds. As a result, delta_Ne40s indicates the electron density fluctuations smaller than 300 km.

    array([ 3811.1 , 16343.3 ,  3246.4 , ...,  1800.45,  1819.9 ,  1701.65])

  • mVTEC
    (Timestamp)
    float64
    51.79 51.77 51.75 ... 20.84 20.94

    units :

    TECU

    description :

    Median of VTEC from all available GPS satellites above 30 degrees.

    array([51.78693356, 51.76898156, 51.74689813, ..., 20.73870507,
           20.8393645 , 20.94156382])

  • IPIR_index
    (Timestamp)
    int32
    7 6 6 6 6 6 6 6 ... 4 4 4 4 4 4 4 4

    units :

    -

    description :

    The numeric index for plasma fluctuations and irregularities: 0-3 low, 4-5 medium, and > 6 high level of ionosphericplasma irregularities.

    array([7, 6, 6, ..., 4, 4, 4], dtype=int32)

  • mROTI20s
    (Timestamp)
    float64
    0.002676 0.002732 ... 0.0114

    units :

    TECU/s

    description :

    Median of Rate Of change of TEC Index (ROTI) from all available GPS satellites above 30 degrees. The ROTI of each satellite is the standard deviation of ROT over 20 seconds.

    array([0.00267555, 0.00273238, 0.00274996, ..., 0.01015663, 0.01085673,
           0.01140045])

  • Grad_Ne_at_PCP_edge
    (Timestamp)
    float64
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    units :

    cm^-3/m

    description :

    The linear electron density gradient calculated over the edges of a patch. This variable is non-zero only at the edges of polar cap patches.

    array([0., 0., 0., ..., 0., 0., 0.])

  • Num_GPS_satellites
    (Timestamp)
    int32
    4 4 4 4 4 4 4 4 ... 6 6 6 6 6 6 6 6

    units :

    -

    description :

    Total number of tracked GPS satellites above 20 degrees.

    array([4, 4, 4, ..., 6, 6, 6], dtype=int32)

  • mROTI10s
    (Timestamp)
    float64
    0.001472 0.001386 ... 0.005609

    units :

    TECU/s

    description :

    Median of Rate Of change of TEC Index (ROTI) from all available GPS satellites above 30 degrees. The ROTI of each satellite is the standard deviation of ROT over 10 seconds.

    array([0.0014718 , 0.00138551, 0.00131007, ..., 0.00471655, 0.00559606,
           0.00560861])

  • IBI_flag
    (Timestamp)
    int32
    -1 -1 -1 -1 -1 ... -1 -1 -1 -1 -1

    units :

    -

    description :

    Plasma Bubble Index, copied from the level-2 Ionospheric Bubble Index product, IBIxTMS_2F.

    array([-1, -1, -1, ..., -1, -1, -1], dtype=int32)

  • Background_Ne
    (Timestamp)
    float64
    1.344e+06 1.344e+06 ... 4.29e+05

    units :

    cm^-3

    description :

    Background density, as calculated from Ne using a percentile filter of 551 datapoints (2 Hz raw data) and 35 as the percentile.

    array([1343599.375  , 1343599.375  , 1343599.375  , ...,  422318.09375,
            425527.09375,  429034.6875 ])

  • Grad_Ne_at_50km
    (Timestamp)
    float64
    -0.4039 0.1449 ... 0.9347 0.9775

    units :

    cm^-3/m

    description :

    The electron density gradient in a running window calculated via linear regression over 13 data points for the 2 Hz electron density data.

    array([-0.40394   ,  0.1448767 , -0.1237337 , ...,  0.89959927,
            0.93474374,  0.97747502])

  • PCP_flag
    (Timestamp)
    int32
    0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0

    units :

    -

    description :

    The polar cap patch flag: 0 if the plasma density measurement occurred OUTSIDE a polar cap patch. 1 if the plasma density measurement occurred at one of the edges of a polar cap patch (no plasma velocity measurements are available). 2 if the plasma density measurement occurred at the LEADING edge of a polar cap patch. 3 if the plasma density measurement occurred at the TRAILING edge of a polar cap patch. 4 if the plasma density measurement occurred INSIDE a polar cap patch proper. When no ion drift data is available, the leading and trailing edges cannot be distinguished. In this case the polar cap patch flag is set to 4 throughout the patch proper and to 1 throughout both edges.

    array([0, 0, 0, ..., 0, 0, 0], dtype=int32)

  • Ne_quality_flag
    (Timestamp)
    int32
    20000 20000 20000 ... 10000 10000

    units :

    -

    description :

    Quality flag for the Ne data and the derived data from Ne, e.g., background density, foreground density etc.

    array([20000, 20000, 20000, ..., 10000, 10000, 10000], dtype=int32)

  • Radius
    (Timestamp)
    float64
    6.84e+06 6.84e+06 ... 6.835e+06

    units :

    m

    description :

    Position in ITRF - Radius.

    array([6840394.97965252, 6840403.88651707, 6840412.74948835, ...,
           6835430.75082371, 6835442.26034348, 6835453.76761761])

  • RODI20s
    (Timestamp)
    float64
    7.764e+03 7.181e+03 ... 943.2 907.9

    units :

    cm^-3/s

    description :

    Rate Of change of Density Index (RODI) is the standard deviation of ROD over 20 seconds.

    array([7764.00253246, 7181.49622793, 7181.49622793, ...,  923.42601747,
            943.21446108,  907.90485614])

  • delta_Ne20s
    (Timestamp)
    float64
    1.027e+04 2.831e+03 ... 1.702e+03

    units :

    cm^-3

    description :

    Derived by subtracting Ne by its median filtered value in 20 seconds. As a result, delta_Ne20s indicates the electron density fluctuations smaller than 150 km.

    array([10266.5 ,  2830.85,     0.  , ...,  1800.45,  1819.9 ,  1701.65])

  • TEC_STD
    (Timestamp)
    float64
    3.131 3.122 3.114 ... 2.866 2.891

    units :

    TECU

    description :

    STD of VTEC from all GPS satellites.

    array([3.13145112, 3.12249362, 3.11383   , ..., 2.8407127 , 2.86645736,
           2.89068334])

  • delta_Ne10s
    (Timestamp)
    float64
    67.88 1.296e+04 ... 1.702e+03

    units :

    cm^-3

    description :

    Derived by subtracting Ne by its median filtered value in 10 seconds. As a result, delta_Ne10s indicates the electron density fluctuations smaller than 75 km.

    array([   67.875, 12961.6  ,     0.   , ...,  1800.45 ,  1819.9  ,
            1701.65 ])

• Indexes: (1)
  • Timestamp
    PandasIndex

    PandasIndex(DatetimeIndex(['2014-12-21 00:00:00.196999936',
                   '2014-12-21 00:00:01.196999936',
                   '2014-12-21 00:00:02.196999936',
                   '2014-12-21 00:00:03.196999936',
                   '2014-12-21 00:00:04.196999936',
                   '2014-12-21 00:00:05.197007872',
                   '2014-12-21 00:00:06.196999936',
                   '2014-12-21 00:00:07.197007872',
                   '2014-12-21 00:00:08.196999936',
                   '2014-12-21 00:00:09.197007872',
                   ...
                   '2014-12-21 02:59:50.196999936',
                   '2014-12-21 02:59:51.196999936',
                   '2014-12-21 02:59:52.197007872',
                   '2014-12-21 02:59:53.196999936',
                   '2014-12-21 02:59:54.197007872',
                   '2014-12-21 02:59:55.196999936',
                   '2014-12-21 02:59:56.197007872',
                   '2014-12-21 02:59:57.196999936',
                   '2014-12-21 02:59:58.196999936',
                   '2014-12-21 02:59:59.196999936'],
                  dtype='datetime64[ns]', name='Timestamp', length=10800, freq=None))

• Attributes: (3)

  Sources :

  ['SW_OPER_IPDAIRR_2F_20141221T000000_20141221T235959_0302']

  MagneticModels :

  []

  AppliedFilters :

  []

Alternative plot setup

To plot the data from xarray, we need a different plotting setup. This does however give us more control over the plot. The units are extracted directly from the xarray object.

fig, axes = plt.subplots(nrows=7, ncols=1, figsize=(20, 18), sharex=True)


def subplot(ax=None, y=None, **kwargs):
    """Plot combination of variables onto a given axis"""
    units = ds[y[0]].units
    for var in y:
        ax.plot(ds["Timestamp"], ds[var], label=var, **kwargs)
        if units != ds[var].units:
            raise ValueError(f"Units mismatch for {var}")
    ax.set_ylabel(f"[{units}]")
    # Reformat time axis
    # https://www.earthdatascience.org/courses/earth-analytics-python/use-time-series-data-in-python/customize-dates--matplotlib-plots-python/
    ax.xaxis.set_major_formatter(DateFormatter("%Y-%m-%d\n%H:%M:%S"))
    ax.legend(loc="upper right")
    ax.grid()


subplot(ax=axes[0], y=["Background_Ne", "Foreground_Ne", "Ne"])
subplot(ax=axes[1], y=["Grad_Ne_at_100km", "Grad_Ne_at_50km", "Grad_Ne_at_20km"])
subplot(ax=axes[2], y=["RODI10s", "RODI20s"])
subplot(ax=axes[3], y=["ROD"])
subplot(ax=axes[4], y=["mROT"])
subplot(ax=axes[5], y=["delta_Ne10s", "delta_Ne20s", "delta_Ne40s"])
subplot(ax=axes[6], y=["mROTI20s", "mROTI10s"])
fig.subplots_adjust(hspace=0)